The fog server in a fog computing paradigm extends cloud services to latency-sensitive tasks by employing fog nodes (FNs) near user devices. The resource-constrained FNs face the challenge of meeting stringent deadlines of latency-sensitive tasks. The completion deadline of such tasks becomes critical on preemption. Task preemption is unavoidable in uncertain events, such as FN hostility, overloading, and mobility of the host FN or the user device. Rescheduling the task that is likely to face preemption is a better solution than terminating it. This paper proposes a rescheduling algorithm for the fog server to reschedule preempted tasks to FNs that can serve them to completion within their expected time. The rescheduling algorithm aims to attain a rescheduling list that guarantees the task deadline requirements. The brain-inspired rescheduling decision-making (BIRD) algorithm proposed in this paper uses the actor-critic reinforcement learning method for rescheduling preempted tasks to FNs. It mimics the decision-making model of the human brain to control voluntary motor activity. It guarantees the deadline requirement of the preempted task by ensuring the optimal performance of the FN through load balancing while rescheduling the preempted tasks to FNs. Experimental evaluation shows that the BIRD algorithm offers better FN selection than other scheduling policies such as first come first served (FCFS), greedy task allocation, task allocation based on least laxity, shortest job first (SJF), and earliest deadline first (EDF).

雾计算范式中的雾服务器通过在用户设备附近使用雾节点 (FN) 将云服务扩展到对延迟敏感的任务。资源受限的 FN 面临着满足延迟敏感任务的严格期限的挑战。此类任务的完成期限对抢占至关重要。任务抢占在不确定事件中是不可避免的，例如 FN 敌对、过载和主机 FN 或用户设备的移动性。重新安排可能面临抢占的任务是比终止它更好的解决方案。本文提出了一种雾服务器的重新调度算法，用于将抢占任务重新调度到 FN 上，以便在预期时间内完成任务。重新调度算法旨在获得保证任务期限要求的重新调度列表。本文提出的类脑重新调度决策（BIRD）算法使用actor-critic强化学习方法将抢占任务重新调度到FN。它模仿人脑的决策模型来控制自主运动活动。它通过负载均衡确保 FN 的最佳性能，同时将抢占任务重新调度到 FN，从而保证抢占任务的截止时间要求。实验评估表明，BIRD 算法提供了比其他调度策略更好的 FN 选择，例如先到先服务 (FCFS)、贪婪任务分配、基于最小松弛度的任务分配、最短作业优先 (SJF) 和最早截止日期优先 (EDF) .